scikit-learn random state in splitting dataset

Question

Can anyone tell me why we set random state to zero in splitting train and test set.

X_train, X_test, y_train, y_test = \\
    train_test_split(X, y, test_size         


        

Answer 1

The random_state is an integer value which implies the selection of a random combination of train and test. When you set the test_size as 1/4 the there is a set generated of permutation and combination of train and test and each combination has one state. Suppose you have a dataset---> [1,2,3,4]

Train   |  Test   | State
[1,2,3]    [4]      **0**
[1,3,4]    [2]      **1**
[4,2,3]    [1]      **2**
[2,4,1]    [3]      **3**

We need it because while param tuning of model same state will considered again and again. So that there won't be any inference with the accuracy.

But in case of Random forest there is also similar story but in a different way w.r.t the variables.